Built Environment and Health: A Focus on
Neighborhoods

A recent body of literature suggests that in
addition to human exposures to chemical releases, a community’s land use
pattern, layout, and design can influence behaviors and impact health.[381]
The term “built environment” describes the constructed places people use and
consists of things like buildings, transportation systems, and open
spaces. Relationships between the built environment and health do not fit
into the release-exposure-outcome model that has been presented thus far.
However, several characteristics of neighborhood-level built environment
features are considered in this report because they are very much a part of the
physical environment around us. This section, therefore, presents the view that
the places where we live, work, and play can be risk factors for multiple
health outcomes like injuries, respiratory conditions, and obesity.
Figure 6 shows specific theorized connections between health outcomes and
neighborhood-level built environment features. Indoor environments also
affect health but, as mentioned in the Introduction, are not discussed in this
report. However, an important consideration for neighborhoods are that
they are not created equally; significant differences exist between rural,
suburban, and urban areas. In addition, predominantly low-income and/or minority
communities suffer a disproportionate share of lower quality housing,
unmaintained public spaces, and closer proximity to polluting sources.[382]

Figure
6: Examples of Health Outcomes that May be Related to Built Environments at
the Neighborhood Level

Built Environment Characteristic

Consequence or Health Outcome

Residential Factors

Layout
of community’s features including building heights and scale, street
connectivity, density, and land use

May influence a person to drive, walk, or bike which
affects physical activity and stress levels as well as ambient air
quality, which relates to the occurrence and severity of respiratory
illnesses

Appearance of local environment including vacant housing,
graffiti, and litter

Presence and good condition of bike lanes, sidewalks

Business and Other Amenities

Access to trails, parks, public pools, recreation and
senior centers

May influence individuals’ engagement in recreational
physical activities to prevent obesity and other chronic conditions

Many built environment-health linkages have been
examined using GIS (Geographic Information Systems). A geographic information
system is a tool for management, analysis, and display of features and
attributes of places. GIS is helpful because it visually captures the
presence of features related to residential areas, business locations, and
transportation systems, which can be analyzed in relation to income, health
outcomes, or any other attribute that can be displayed spatially.

The following pages highlight selected literature
and available data sources as they relate to the built environment. To
date, most research in this field has been in the form of cross-sectional
studies that analyze conditions at a single point in time and can therefore
indicate associations with health outcomes, but not causality.[383]
Other challenges of built environment research include (1) determining the
relative impacts of multiple changes in an area over time,[384] (2) determining the relative impacts of
local and regional factors, and (3) determining the spectrum of individual
reactions to a given environment (e.g., women, children, and the elderly may be
more affected by fear of crime or violence).[385] Better available data is sure to
aid the development of the evidence base relating the built environment to
health.

Due to limitations of time and resources, the focus
has been placed on information obtainable for the City of Pittsburgh. Although
differences between urban and rural environments are important, they are not
covered by the present report. The remainder of the section is organized under
three main headings: Residential Characteristics, Business and Other
Amenities, and Transportation. For a listing of data sources for
neighborhood level, built environment characteristics, see Figure 7 at the end
of this section.

Residential Characteristics

This section includes a discussion of urban sprawl,
neighborhood appearance and walkability.

Urban Sprawl

Urban sprawl refers to the mass suburbanization
that has dominated the American landscape since the advent of the interstate
highway system after World II.[386] In contrast to urban areas,
sprawl is characterized by low density (few people living on large parcels),
low land use mix (large areas with similar zoning and use), low connectivity of
roads (lack direct routes to destinations), and lack of a downtown center.[387]

Evidence of the environmental and social impacts
of sprawl is well established.[388]
However, recent research shows that urban sprawl may also be associated with
poor health outcomes where those living in sprawling communities are likely
to walk less, weigh more,[389]
and have greater prevalence of hypertension than those living in compact counties.[390]
There may also be associations between urban sprawl and mental health.[391]

Because there are serious environmental, social,
economic, and health consequences of sprawl, it is important that we be able
to measure it. To do so, we need data. On a regional scale, several
indices have been developed that use similar data sources, but reveal different
conclusions. For example, The Brookings Institution’s “Back to Prosperity”
used the indicator of land urbanized per new household to reveal the worrisome
trend that the Pittsburgh Metropolitan Area is by far the worst sprawling
area in the country.[392] Smart Growth America developed the
Metropolitan Sprawl Index, which integrates 22 variables describing residential
density, land use mix, degree of centering, and street accessibility.
The corresponding county sprawl index uses only 6 of these variables, where
Allegheny County was slightly more compact than the average county and Beaver,
Fayette, Washington, and Westmoreland are at or slightly below the average
score of 100.

Both of these indicators use data from the US
Census Bureau to assess population and household changes. To determine
land use patterns, both indicators also accessed data from the Natural
Resources Inventory (NRI), which is a spatial survey of all non-federal U.S.
lands. It was conducted by the U.S. Department of Agriculture every five
years between 1982 and 2000 but was changed to an annual survey in 2001.
Using photo-interpretation and other remote sensing methods, statistically
sampled locations are labeled with a mutually exclusive category of use.
Data can be used to estimate land use trends for 1982, 1987, 1992, and 1997 for
multi-county geographic areas. Currently, the more recent 2001 and 2002
data are only available for larger geographic areas because of a smaller sample
size of locations.[393]

Other data sources used by the Metropolitan Sprawl
Index include the following:[394]

·American Housing Survey is conducted by the US Census Bureau
for the Department of Housing and Urban Development. Pittsburgh is one
of the 47 metropolitan areas surveyed for information about housing, household
characteristics, equipment, fuels, recent movers, and neighborhood quality.
The most recent data available online are for 1995.

·Zip Code Business Patterns are extracted from the Standard
Statistical Establishments List, a file of all single and multi-establishment
companies created by the U.S. Census Bureau. Data are provided on the
total number of establishments, employment, and payroll for more than 40,000
zip codes nationwide. The number of establishments is broken down into 9
employment size categories by detailed industry for each zip code.

·Census TIGER (Topologically Integrated Geographic Encoding and
Referencing) files are a digital database of geographic features such as
streets, railroads, lakes, and political boundaries. To make use of the
data, a user must have Geographic Information System (GIS) software that can
import these files. With the appropriate software, a user can produce
digital street maps and generate measures of street density and block length.

Land use trends over time can also be analyzed
using data extracted from satellite images. For example, the Landsat
Thematic Mapper can differentiate between 15 land use characteristics.[395]
Advantages of using this spatially detailed data source include the ability to
examine the whole landscape, assess urban growth in all areas, and depict
trends. To provide the most realistic representation of landscape patterns,
information should be calculated to the smallest point (per pixel) and avoid
spatial averaging over a large geographic area. With this fine
grained data, it is possible to not only identify how much and what kind of
change has occurred but where it is in relation to other classes of urban
development and existing land cover types.[396]

Neighborhood Appearance and Safety
Concerns

There is some evidence that suggests neighborhood
appearance influences certain behavioral and psychological responses.
Broken windows, abandoned buildings, graffiti, illegal drug sales,
prostitution, and lack of green space have been associated with increased
criminal activity.[397][398]
. In addition, recent literature suggests that age of the housing stock[399]
and other aesthetic features of local environments are correlated with levels
of walking[400]
and youth recreational activity.[401] Age of the housing stock also
serves as a useful indicator to measure the potential risk of childhood lead
poisoning.

Neighborhood appearance is a rather subjective
term, but it can be measured using both objective data (e.g., locations of
vacant housing) and perceptual data (e.g., residents’ feelings of
safety). Sources of objective measures may either come from direct
observations of features in communities or from existing databases that may
track certain maintenance activities or complaints. The perceived
physical environment may be assessed using population-based surveys and
surveillance systems with responses aggregated to a small geographic area
(e.g., census block or tract).[402]

There are limitations to both types of data
sources.[403]
For example, resident’s self-reports of features in their neighborhood can
display less variation than do objective assessments because few may want to
admit that it’s substandard. Regardless of potential bias, which
can be minimized with proper survey design, perceptual data can reveal very
different information than what is possible to measure objectively.[404]

The U.S. Census Bureau is a popular source for
housing characteristics such as age of household and vacancy rates because it
contains detailed information for relatively small areas of geography and is
also consistent across the country. When analyzing trends over time with
census data, adjustments in the tract boundaries should be identified because
such adjustments can distort data presentation and conclusions. Other
limitations include its ten-year timeframe and lack of parcel level
information, which can be helpful when analyzing connections between the built
environment and health.

One source of local, parcel level data can be
provided by the Community Information System (CIS) Project, mentioned earlier
under the “Existing Endeavors” section. Currently in its second phase of
development, the project has surveyed nearly 10,000 parcels of real estate in
approximately 18 neighborhoods throughout the City of Pittsburgh.
Eventually, the condition and vacancy status of all parcels in the city will be
documented using a combination of data including observations from trained
field workers and administrative records related to blight, disinvestment,
investment, and land use. These sources of secondary data include
multiple departments within the City of Pittsburgh, Allegheny County Health
Department, lien holders, and utility companies. The project will not
only centralize data from multiple data holders, but will also provide the
ability for all stakeholders to understand how vacant housing impacts
communities. As the project moves forward, it
will be possible to add other data sources to provide even greater utility to
anyone interested in community and neighborhood level characteristics.[405]

Walkability and Bikeability

Walking and biking offer multiple health
benefits. Regular physical activity is associated with lower death rates
for adults of any age, decreased risk of death from heart disease, lowered risk
of developing diabetes, and decreased risk of colon cancer. Children and
adolescents need weight-bearing exercise for normal skeletal development, and
young adults need such exercise to achieve and maintain peak bone mass. In
addition, older adults can improve and maintain strength and agility with
regular physical activity, which can reduce the risk of falling and help
maintain independent living.[406]

Interestingly, characteristics of the built
environment found to be associated with walking for transportation differ from
those associated with walking for exercise or recreational purposes.[407]Regardless, many of these environmental features
have already been mapped by local universities and city, county, and state
departments. In addition, walkability and bikeability audits can reveal
more detailed, street level information (e.g., width, gradient, and condition
of surfaces). However, collecting these data is time and labor intensive,
but training interested community members as field workers can offset some
costs, while also engaging those who have the most to gain from improvements to
their neighborhoods. These audits can also help identify unsafe
conditions for pedestrian and bicyclists since walking is by far the most
dangerous mode of travel per mile, especially in sprawling communities where
streets are built for motor vehicle use only. Streets without safe places
to walk and bicycle put people at risk, particularly children who are
especially vulnerable and minority populations who suffer a disproportionate
share of traffic fatalities.[408]
There are a wide variety of data sources available to identify safety trends
for motorists, pedestrians, and bicyclists for large geographic areas such as
county, state, or nation. These include the Centers for Disease Control,
Pennsylvania Department of Transportation, and National Highway Traffic Safety
Administration.[409] The Fatality Analysis
Reporting System (FARS) is a nationwide reporting system that tracks all motor
vehicle traffic crashes that occur on a public right of way and result in the
death of a vehicle’s occupant or a non-motorist (pedestrian or bicyclist)
within 30 days of the crash. Information from multiple sources is
compiled and standardized. However, to protect individual privacy, no
personal information, such as names, addresses, or specific crash locations is
coded. Race was recently added as a field to FARS
database.[410]

The Pedestrian Danger Index referenced in the Mean
Streets 2004 report, published by the Surface Transportation Policy Project,
combines pedestrian fatality data from the FARS system with journey to work
information from the U.S. Census. The Pittsburgh Metropolitan Statistical
Area was ranked in the top 50 for dangerous streets. One limitation of
the index is that it only includes fatalities, so accidents that result in
minor or even serious injuries are omitted from the calculation. The same
datasets could potentially be used to develop a bicyclist danger index.

Using data from the Crash Outcome Data Evaluation
System (CODES) is a potential way to assess the severity of injuries resulting from
a motor vehicle crash. This dataset combines police reports from the
crash scene with injury outcome data collected at the scene, en route to the
emergency department, at the hospital or trauma center, and after
discharge. CODES data often contain information related to the crash
location such as county, census tract, police beat, street name or address,
distance from a milepost or nearest intersection which permits a spatial
analysis. Aggregate reports are available via the Internet, but due to
confidentiality concerns, raw data are not readily available over the
web. The CODES Board of Directors makes all decisions related to
management and release of the linked data.[411]

Because it is difficult to obtain specific accident
locations for a local level, a group of cyclists known as Ghost Bike has
started to collect self-reported accident data from bicyclists using an online
form.[412]
Unfortunately, due to limited financial and technical resources, they face
multiple challenges with collection and analysis of this data.

Businesses and Other Amenities

As already mentioned, stores, post offices, parks,
and other amenities that are within walking distance increase the likelihood of
getting out on foot instead of using a vehicle. However, there are other
health implications because certain amenities may be more convenient in some
areas while less so in others. An analysis of the locality of food retailers
in relation to neighborhood wealth revealed that there are more supermarkets,
fewer fast food restaurants, and fewer taverns in the wealthier neighborhoods
compared to the poorest areas. It has also been shown that urban
dwellers pay 3% to 37% more for groceries compared to suburban residents.[413]
Thus, financial and transportation barriers may limit people’s ability to
purchase nutritious foods. This is especially relevant for those living
in predominantly minority or low-income neighborhoods. A 2003 CarnegieMellonHeinzSchool
graduate project mapping AlleghenyCounty grocery stores
over population and economic characteristics yielded similar findings.[414]

Business locations as well as other features of the
built environment including house size, street lights, and billboards are
regulated by zoning guidelines. In order to implement the new Urban
Zoning Code adopted in 1999, The City of Pittsburgh’s Map Pittsburgh Project
has collected land use information from approximately 1/3 of the city’s total
parcels using trained community workers. This information is useful in
understanding the mixture of uses in a neighborhood. Even though there
are over 150 categories of land use, this inventory typically can’t
differentiate between a shoe store or a coffee shop. To obtain the
specific type of business or amenities present, addresses need to be compiled
using sources such as Reference USA or online yellow pages. These
addresses can be geocoded (entered into a GIS program), and then mapped using
spatial software. As shown in Figure 7, this process has already been
completed for common amenities like grocery stores, farmer’s markets, schools,
and recreational and senior centers. Once mapped in this method, it is
possible to assess the number and types of destinations in an area, as well as
distance and shortest route to them.

Transportation

Mobile emissions from cars and other vehicles are a
significant contributor to air pollution both on a regional scale and at the
street level Clean Air Task Force recently released a report
documenting the health risks related to exposure to diesel exhaust.
Children and seniors are particularly vulnerable populations. Those who
operate diesel machinery, live near roadways which accommodate diesel vehicles,
frequently ride on school or transit busses, and commute daily in heavy traffic
also face potentially greater risks for respiratory and cardiovascular
diseases, lung cancer, as well as premature death.[415]

A combination of mechanical and human methods of
data collection can determine the number of cars traveling on a road or people
using a trail. The Pennsylvania Department of Transportation’s Internet
Traffic Monitoring System[416]
collates data from partners who collect counts from state and federally funded
local roads. Users of the site can choose from 12 different reports for a
municipality, zip codes, intersection, PennDot route, or street name.
Typically, data are updated on a 1-, 3-, or 5-year timeframe depending upon the
road. About 30% of this raw data distinguishes between cars and trucks,
but estimates are made based on this information for other road segments.[417]
Numbers of bicycles on the road, on the other hand, are harder to obtain using
mechanical counters and usually must rely on observational methods because
there are fewer of them on the road.[418]

A variety of datasets can be utilized to help
determine transportation patterns. The U.S. Census provides data on
means of transportation to work, but other destinations are not included.
Regional travel surveys have been conducted throughout the country as a means
to fill in these gaps. Locally, the Southwestern PA Commission (SPC)[419]
recently completed a survey of 2,500 households to learn of transportation
patterns. Participants filled out travel diaries detailing all types of
transportation modes (car, bike, foot) for all destination points (school,
grocery stores, etc.).[420]SPC has
published a summary report and will be analyzing the data more closely to build
a local origin-destination model for the 10-county region. Accessing the
raw data may be possible once quality issues have been addressed.[421]

The Healthy
Neighborhoods Project (HNP), a partnership between public housing residents
living in Pittsburg, CA and the local health department, exemplifies how
innovative methods of data collection can in themselves yield positive outcomes.
An initial step of this project was to train interested residents in skills
such as interviewing, public speaking, and community building. The group
then developed an asset/capacity map as a way of collecting and displaying
information about the physical and social characteristics of the
neighborhood. In order to gather qualitative and perceptual data, a
door-to-door survey was conducted by residents to assess local people’s
abilities (e.g., political involvement, event organization experience, etc.).
In addition, a community mapping day was organized wherein both positive and
negative features of built environment would be inventoried. On this day,
teams of youth and adults identified places selling tobacco, alcohol, and
nutritious foods, as well as local businesses, transportation networks, parks,
and other amenities. Specific findings included the identification of parks
that needed to be made safe for kids to play, a building that could potentially
be renovated for a new community center and churches that could be mobilized to
improve the neighborhoods.

HNP’s organizing efforts
produced a variety of both social and environmental changes in the
community. For example, as a result of this project, tobacco billboards
within the neighborhood were removed and funding was secured to develop
workforce development programs, drug elimination activities, and establish a
children’s soccer league.

This brief case study
illustrates the following points:

·A combination of observational and perceptual data may sometimes better
reveal the eco-social context of community environmental health issues.

·The participatory community appraisal process can itself be an
intervention. For example, through this process, community members may learn
organizing and other skills, may gain ability to identify problems and to take
action, and may even begin to feel more a part of their own communities. The
participatory process can also help public agencies to build partnerships with
communities to improve health.

·While a focus that is solely related to data about problems and needs
may be disempowering, identification of data about community assets and
capacity may empower local residents to identify solutions that they themselves
can implement.